The stellar populations of quiescent ultra-diffuse galaxies from optical to mid-infrared spectral energy distribution fitting
Bibliographic record
Abstract
ABSTRACT We use spectral energy distribution (SED) fitting to place constraints on the stellar population properties of 29 quiescent ultra-diffuse galaxies (UDGs) across different environments. We use the fully Bayesian routine PROSPECTOR coupled with archival data in the optical, near, and mid-infrared from Spitzer and Wide-field Infrared Survey Explorer under the assumption of an exponentially declining star formation history. We recover the stellar mass, age, metallicity, dust content, star formation time scales, and photometric redshifts (photo-zs) of the UDGs studied. Using the mid-infrared data, we probe the existence of dust in UDGs. Although its presence cannot be confirmed, we find that the inclusion of small amounts of dust in the models brings the stellar populations closer to those reported with spectroscopy. Additionally, we fit the redshifts of all galaxies. We find a high accuracy in recovering photo-zs compared to spectroscopy, allowing us to provide new photo-z estimates for three field UDGs with unknown distances. We find evidence of a stellar population dependence on the environment, with quiescent field UDGs being systematically younger than their cluster counterparts. Lastly, we find that all UDGs lie below the mass–metallicity relation for normal dwarf galaxies. Particularly, the globular cluster (GC)-poor UDGs are consistently more metal-rich than GC-rich ones, suggesting that GC-poor UDGs may be puffed-up dwarfs, while most GC-rich UDGs are better explained by a failed galaxy scenario. As a byproduct, we show that two galaxies in our sample, NGC 1052-DF2 and NGC 1052-DF4, share equivalent stellar population properties, with ages consistent with 8 Gyr. This finding supports formation scenarios where the galaxies were formed together.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".